Physics-Informed Deep Learning for Virtual Rail Train Trajectory Following Control

Physics-Informed Deep Learning for Virtual Rail Train Trajectory Following Control Physics-Informed Deep Learning for Virtual Rail Train Trajectory Following Control In recent years, advancements in artificial intelligence have reshaped the way we approach complex control systems, especially in the transportation sector. One of the most promising applications is the use of Physics-Informed Deep Learning (PIDL) for Virtual Rail Train Trajectory Following Control . This approach combines the predictive power of machine learning with the reliability of physical laws, providing a robust solution for real-time trajectory control in autonomous rail systems. Virtual Rail Technology Virtual rail technology refers to a system where trains follow a digitally defined path without requiring physical rails for guidance. Instead, trains use sensors, GPS, and control algorithms to stay within a virtual corridor, much like how autonomous cars follow lanes using camera and sensor data. This inn...